Autotuning Method of Membership Function in a Fuzzy Learning Controller

Article type: Research Article

Authors: Chung, Byeong-Mook | Oh, Jun-Ho

Affiliations: Department of Precision, Engineering and Mechatronics, Korea Advanced Institute of Science and Technology, 373-1 Kusong-dong Yusong-gu, Taejon 305-701, Korea

Abstract: A learning algorithm for fuzzy inference rules that can tune both the control input and the linguistic membership function of a fuzzy controller is presented. The algorithm introduces a reference model to generate a desired output and minimizes a performance index based on the error between the reference and plant output. To minimize the performance index, the control input and the membership functions are simultaneously updated by a gradient method. Using the algorithm, we can easily achieve good control rules with a minimal amount of prior information about the environment. In particular, when this algorithm is applied to a tracking problem with a nonlinear plant we obtain efficient control rules with fewer number of rules and higher learning speed than those of the fixed membership function.

DOI: 10.3233/IFS-1993-1408

Journal: Journal of Intelligent and Fuzzy Systems, vol. 1, no. 4, pp. 335-349, 1993